{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "poisson-distribution-and-poisson-process-tutorial.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/towardsai/tutorials/blob/master/poisson-distribution-process/poisson_distribution_and_poisson_process_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ojZQVTJDrUw"
      },
      "source": [
        "# Diving Into the Poisson Distribution and Poisson Process\r\n",
        "\r\n",
        "* Tutorial: https://news.towardsai.net/pd\r\n",
        "* Github: https://github.com/towardsai/tutorials/tree/master/poisson-distribution-process\r\n",
        "\r\n",
        "A TV Assembly unit performs a defects analysis task to understand the number of defects that could happen for a given defective TV. Using past quality and audit data is noted that 12 defects are marked on an average for a defective TV, calculate below:\r\n",
        "\r\n",
        "* The probability that a defective laptop has exactly 5 defects.\r\n",
        "* The probability that a defective laptop has less than 5 defects.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvkkNheCDnH1"
      },
      "source": [
        "import numpy as np\r\n",
        "import scipy.stats as stats\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtnc1L2HD7TL",
        "outputId": "0ed2e42e-a17c-4080-a4d4-be3596901a18"
      },
      "source": [
        "n = np.arange(0,30)\r\n",
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqcO28-iEA-N"
      },
      "source": [
        "rate = 12\r\n",
        "poisson = stats.poisson.pmf(n,rate)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HiQUltIvEGa3",
        "outputId": "ebb9e923-e158-41c9-f512-ae9f844321e7"
      },
      "source": [
        "poisson"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.14421235e-06, 7.37305482e-05, 4.42383289e-04, 1.76953316e-03,\n",
              "       5.30859947e-03, 1.27406387e-02, 2.54812775e-02, 4.36821900e-02,\n",
              "       6.55232849e-02, 8.73643799e-02, 1.04837256e-01, 1.14367916e-01,\n",
              "       1.14367916e-01, 1.05570384e-01, 9.04889002e-02, 7.23911201e-02,\n",
              "       5.42933401e-02, 3.83247107e-02, 2.55498071e-02, 1.61367203e-02,\n",
              "       9.68203217e-03, 5.53258981e-03, 3.01777626e-03, 1.57449196e-03,\n",
              "       7.87245981e-04, 3.77878071e-04, 1.74405263e-04, 7.75134504e-05,\n",
              "       3.32200502e-05, 1.37462277e-05])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmF00PoSEKap",
        "outputId": "46e16547-91af-43ed-f1e5-44f6dc3a4541"
      },
      "source": [
        "poisson[5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.012740638735861376"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJTvIzv-EN6q",
        "outputId": "cbcd67cd-14ff-48ac-fd1d-920d74950a72"
      },
      "source": [
        "poisson[0] + poisson[1] + poisson[2] + poisson[3] + poisson[4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.007600390681067"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "iJv5zZrbEQcZ",
        "outputId": "af6de39f-641e-49f5-c043-89938405e9da"
      },
      "source": [
        "plt.plot(n,poisson, 'o-')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}